Introduction to Dataflow Computing

This chapter presents the possibilities for obtaining significant performance gains based on advanced implementations of algorithms using the dataflow hardware. A framework built on top of the dataflow architecture that provides tools for advanced implementations is also described. In particular, the authors point out to the following issues of interest for accelerating algorithms: (1) the dataflow paradigm appears as suitable for executing certain set of algorithms for high performance computing, namely algorithms that work with big data, as well as algorithms that include a lot of repetitions of the same set of instructions; (2) dataflow architecture could be configured using appropriate programming tools that can define hardware by generating VHDL files; (3) besides accelerating algorithms, dataflow architecture also reduces power consumption, which is an important security factor with edge computing.

A generic synchronous dataflow architecture to rapidly prototype and deploy robot controllers

The article presents a software architecture to optimize the process of prototyping and deploying robot controllers that are synthesized using model-based design methodologies. The architecture is composed of a framework and a pipeline. Therefore, the contribution of the article is twofold. First, we introduce an open-source actor-oriented framework that abstracts the common robotic uses of middlewares, optimizers, and simulators. Using this framework, we then present a pipeline that implements the model-based design methodology. The components of the proposed framework are generic, and they can be interfaced with any tool supporting model-based design. We demonstrate the effectiveness of the approach describing the application of the resulting synchronous dataflow architecture to the design of a balancing controller for the YARP-based humanoid robot iCub. This example exploits the interfacing with Simulink® and Simulink® Coder™.